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Fruiting bodies of the social amoeba Dictyostelium discoideum increase spore transport by Drosophila

smith j, Queller DC, Strassmann JE (2014) Fruiting bodies of the social amoeba Dictyostelium discoideum increase spore transport by Drosophila. BMC Evolutionary Biology 14: 105. doi:10.1186/1471-2148-14-105. Journal link

Background: Many microbial phenotypes are the product of cooperative interactions among cells, but their putative fitness benefits are often not well understood. In the cellular slime mold Dictyostelium discoideum, unicellular amoebae aggregate when starved and form multicellular fruiting bodies in which stress-resistant spores are held aloft by dead stalk cells. Fruiting bodies are thought to be adaptations for dispersing spores to new feeding sites, but this has not been directly tested. Here we experimentally test whether fruiting bodies increase the rate at which spores are acquired by passing invertebrates.

Results: Drosophila melanogaster accumulate spores on their surfaces more quickly when exposed to intact fruiting bodies than when exposed to fruiting bodies physically disrupted to dislodge spore masses from stalks. Flies also ingest and excrete spores that still express a red fluorescent protein marker.

Conclusions: Multicellular fruiting bodies created by D. discoideum increase the likelihood that invertebrates acquire spores that can then be transported to new feeding sites. These results thus support the long-hypothesized dispersal benefits of altruism in a model system for microbial cooperation.

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    Why I don’t care if I’m right

    Recently I’ve been finding that I really don’t care if I’m right. What I mean is: I don’t care if a hypothesis I’ve come up with turns out to be correct. That theory paper about nonadaptive causes of social competition really brought this up. I do care about formulating a hypothesis well, presenting it clearly, empirically testing it in a way that gives it a fair chance to succeed or to fail, and making sure that my conclusions are justified by my data. But whether a hypothesis turns out to be true? That’s up to nature. The data are what the data are.

    The way I see it, if I formulate a hypothesis well, then it’s a reasonable possibility for how things work. If I hadn’t thought of it, I’d want somebody else to. And I’d want to know whether it’s right or wrong whether it was originally my idea or not. That’s one of the main ways we make progress in science—by lining up and knocking down hypotheses until what’s left just won’t fall.

    Of course, it’s way easier to publish positive results, so being right makes the mechanics of science productivity a lot simpler. In practice, what usually happens is that a hypothesis is kind of right, in some circumstances, but not the whole story.

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      Nonadaptive process can create the appearance of facultative cheating in microbes

      smith j, Van Dyken JD, Velicer GJ (2013) Nonadaptive process can create the appearance of facultative cheating in microbes. Evolution 68: 816-826. DOI: 10.1111/evo.12306

      Abstract: Adaptations to social life may take the form of facultative cheating, in which organisms cooperate with genetically similar individuals but exploit others. Consistent with this possibility, many strains of social microbes like Myxococcus bacteria and Dictyostelium amoebae have equal fitness in single-genotype social groups but outcompete other strains in mixed-genotype groups. Here we show that these observations are also consistent with an alternative, nonadaptive scenario: kin selection-mutation balance under local competition. Using simple mathematical models, we show that deleterious mutations that reduce competitiveness within social groups (growth rate, e.g.) without affecting group productivity can create fitness effects that are only expressed in the presence of other strains. In Myxococcus, mutations that delay sporulation may strongly reduce developmental competitiveness. Deleterious mutations are expected to accumulate when high levels of kin selection relatedness relax selection within groups. Interestingly, local resource competition can create nonzero “cost” and “benefit” terms in Hamilton’s rule even in the absence of any cooperative trait. Our results show how deleterious mutations can play a significant role even in organisms with large populations and highlight the need to test evolutionary causes of social competition among microbes.

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        Valve Software on evidence-driven business

        Valve Software is a video game company with an unusual “flat” organizational structure in which there are no managers. I recently read their handbook for new employees looking for ideas about running a lab where the PI is an advisor but not a “boss” that tells people what to do. I came across this passage:

        Screwing up is a great way to find out that your assumptions were wrong or that your model of the world was a little bit off. As long as you update your model and move forward with a better picture, you’re doing it right. Look for ways to test your beliefs. Never be afraid to run an experiment or to collect more data.

        It helps to make predictions and anticipate nasty outcomes. Ask yourself “what would I expect to see if I’m right?” Ask yourself “what would I expect to see if I’m wrong?” Then ask yourself “what do I see?” If something totally unexpected happens, try to figure out why.

        There are still some bad ways to fail. Repeating the same mistake over and over is one. Not listening to customers or peers before or after a failure is another. Never ignore the evidence; particularly when it says you’re wrong.

        It’s nice, and interesting, to see the scientific “mindset” being adopted in the world of business.

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          How do you help people write?

          This year, one of the PIs in my lab has been requiring people to submit regular writing samples. It started off as 1000 words per month and is now at 300 words per week. The idea is that writing is a big part of science and it becomes easier when it’s part of your day-to-day activity. I’ve been writing a lot recently, but I’ve haven’t been sending in the 300 words. I agree with the goals for these writing assignments, but I’m not convinced that their current implementation is the best use of our time and attention.

          • They’re basically homework. They become distracting busy-work when non-writing issues are most relevant to a lab member’s work. Perhaps somebody is struggling with experimental design, or statistics, or the best way to represent their results as figures, or learning to code. More-frequent deadlines make the distraction worse. A large part of the work on my plate is writing, but not everybody is in that position.
          • Assigning frequent writing tasks and then having to pursue people to complete them emphasizes external motivations for writing — sticks wielded by authority. It reinforces negative associations with writing as something unpleasant but required. This seems counterproductive if the goal is to make writing easier. A healthier approach would be something that helps people appreciate writing as an important part of the scientific process. Writing a draft abstract and introduction early on in a project, for example, helps you work out what the main point is and therefore what the most important experiments and controls are. Writing up results as they come helps you understand what you have and the best direction to take the project. A project doesn’t count as scientific knowledge until the paper is published. It can’t be built built upon or meaningfully incorporated into others’ work until it’s citeable. Seeing one’s work published, cited, and recognized improves self-esteem. Going through the peer review process helps you design better projects in the future because you learn to anticipate the issues and concerns your colleagues will have. Grad students will appreciate the fact that committee members are less likely to ask for revisions on chapters already published.
          • Specific word requirements discourage concise language.
          • My impression is that people often do not find the feedback they get on these assignments all that useful. People do need feedback on their writing, but in my experience the most crucial problems are in the structure of arguments, not the specifics of language. Arguments in scientific writing usually take place over the course of several paragraphs — over a whole introduction, or a section of the discussion. Abstracts are one of the few places where a whole argument fits into 300 words. People write differently, but I usually work out my arguments in abbreviated outline form. When I have trouble formulating an argument, what often helps me is to talk it through with someone and bounce ideas off them. Fleshing out an incoherent argument into complete sentences seems like misplaced effort.
          • Assigning tasks and pursuing people to complete them casts PIs as bosses rather than an advisors. This seems counterproductive when the goal is to train independent, self-motivated researchers.

          I’m not sure what a better implementation would be. Perhaps something less rigid and more tailored to each lab member’s work — having people write up the materials and methods for the experiments they’ve just started, for example, or writing the figure legend and results for the data they just finished collecting. I think it’s something worth discussing as a lab, to see what people think would be most useful.

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            Organizing computational projects

            I’ve been working on a computational/bioinformatics project, and I’ve been finding it a little bit challenging to organize and document my work. I’m used to at-the-bench laboratory projects, where you typically have a small handful of discrete experiments, each documented in a pen-and-paper lab notebook. Analyzing the data from each experiment usually doesn’t require much more than an Excel spreadsheet and an R script. But with computational biology, it seems like every little task requires its own dedicated script or command-line program that generates an output file (or three) in their own unique format. It’s very easy to quickly end up with a giant pile of files with no clear record of what they are, where they came from, and why you made them. Coming back to the project after a little time away, it can all be very opaque: What was I doing again? Where did I stop? Why did I stop?

            Here’s what’s working for me right now: I divide the project up into logical chunks, each with its own folder numbered and named after that chunk (“1. Identify foo homologues”, for example, or “2. Phylogeny of foo proteins”). Each folder contains all the files necessary for that chunk of analysis, even if that means duplicating some files. Each folder also has a README.txt file describing what the other files in the directory are and how they were created, ordered by their place in the workflow. Like this:


            Identify foo homologues in Dictyostelid genomes
            jeff smith 2013

            --------------------
            Description of files
            --------------------

            foo.hmm
            - Hidden markov model of the foo domain. Obtained from Pfam protein families database 2013-05-27.

            dicty_primary_protein.fasta
            - Protein-coding sequences in Dictyostelium discoideum genome. Obtained from DictyBase 2013-05-27.

            hmmer_discoideum.out
            - Output of hhmer search for foo domains in D. discoideum genome. Command: hmmsearch foo.hmm dicty_primary_protein.fasta > hmmer_discoideum.out

            hmmer_summary
            - Summary of hhmer results for genes with significant alignment to foo domain model. Used by collate_sequences.R.

            collate_sequences.R
            - R script to compile and rename matched sequences for further analysis

            foo_proteins.fasta
            - Protein sequences of identified foo homologues

            Right now, I’m focusing on making the work intelligible to future readers (including future me). I’m less concerned with keeping an electronic lab notebook that documents the day-to-day details of the analyses I try and how they turn out. Some people use wikis for this, or revision control software. I’m holding off on that for now.

            There’s also “A quick guide to organizing computational biology projects” in PLoS Computational Biology that has some good ideas. I’ve found this part to be especially true:

            “Everything you do, you will probably have to do over again. Inevitably, you will discover some flaw in your initial preparation of the data being analyzed, or you will get access to new data, or you will decide that your parameterization of a particular model was not broad enough. This means that the experiment you did last week, or even the set of experiments you’ve been working on over the past month, will probably need to be redone. If you have organized and documented your work clearly, then repeating the experiment with the new data or the new parameterization will be much, much easier.”

            Noble WS (2012) PLoS Comp Biol 5:e1000424

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              Benchwork in the age of open science

              What does our new age of open science and open data mean for research that’s mainly laboratory benchwork? Most of the major ecology & evolution journals now require that the data for papers also be published in an open-access repository like GenBank (for nucleotide sequences), Treebase (for phylogenetics), and Dryad (for other kinds of data). In at-the-bench wet-lab research, there’s often a fair amount of analysis linking the raw observations, the statistical result, and its biological interpretation. How much of that process should go into a public repository, and in what form?

              In my own field of microbial evolution, one way in which I’d find open data useful has to do with fitness. There are many different ways to measure the survival and reproductive success of organisms, and they each have their uses. I’ve found when reading papers that there are often times in the authors plot their fitness data in one way, and I wonder what it’d look like plotted another way. For example, I often see papers that plots mean group productivity and within-group relative fitness (a multilevel selection partition of social evolution), and I wonder what the data would look as the absolute fitness for each microbial genotype (the neighbor-modulated fitness partition in kin selection theory). Much of the kin selection/group selection debate is about the best way to calculate and think about fitness. I prefer to plot fitness data in a way that’s easily interpreted multiple ways. But it’d be nice to at least grab other people’s data so I can replot it a bit. So here’s my recommendation:

              Make sure that the data you archive includes the raw colony counts (or plaque counts, or cell counts). With that, anybody can easily calculate their favored fitness measure.

              I’ve been working on a project that involves a large amount of flow cytometry data. How much of my data and calculations should go into Dryad? I’m tempted to say: all of it. From the raw data files, to the flow cytometry gating scripts, to the cell counts to the derived values (growth rates, fitness, etc), to the statistical analyses, to the scripts for making the figures. Why not?

              Sharing everything also helps us become better scientists. I often learn a few things when I look at other peoples’ scripts and spreadsheets. I’ve traditionally used Excel for basic data manipulation and plotting. Now, though, I’m thinking that maybe I should try to do as much as possible in R—the scripts are easy to share, it’s easier to use for for large data sets, and it avoids the copy/paste errors that sometimes crop up in spreadsheets.

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                How to write a paper people will cite

                Here’s some great tips for writing scientific papers from Steve Ellner (link to original pdf). Ellner presents them as tips for theoretical papers, but I think they’re good practice for any paper:

                1. Don’t maintain suspense.

                • Present the topic clearly at the very beginning.
                • Explain the relevance of the paper at the very beginning.
                • Quickly telegraph where the entire paper will be going. Give away all your punchlines in the abstract, and do it again in the Introduction.

                2. Make the paper easy to skim.

                • Make sure that the “meat”—the core that everyone should read—is well labeled and easy to find.
                • Explain your main results using graphs.
                • Remove from the main text any technical details that aren’t needed for the flow of ideas. Readers shouldn’t have to stop and think about whether or not they have to think about an equation.
                • Use signposting to help people “peel the onion”—get as deep into the paper as they want, but no deeper. Technical sections should be prefaced by an explanation of what and who it’s for, so it’s easy for a reader to tell if they should read it, skim it, or skip it for now.
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                  Fictitious science

                  One of the drawbacks to being a scientist is that science fiction becomes harder to enjoy. Storytelling requires a certain suspension of disbelief to work. But when your day-to-day job involves asking questions about how the natural world works, sci-fi movies are full of things that take you out of the story and leave you saying “Oh, come on“. I’ve been reminded of this after seeing Ridley Scott’s film Prometheus, a prequel to his deservedly classic Alien. Some of the things I found myself thinking during the movie:

                  • If the aliens seeded life on earth with DNA oligomers (PCR primers, basically) like this computer animation is showing us, why did they have to sacrifice one of themselves to do it? Wouldn’t it be easier to just chemically synthesize them the way we do?
                  • If this is supposed to be the origins of life on earth, then why is it showing us metazoan zygotes? And didn’t they just show us a landscape full of plants, anyway?
                  • Why aren’t any of these characters saying anything about how wierd it is for a rocky moon to have an atmosphere full of oxygen?
                  • If this is a barren moon, then why are there earthworms? And why are the earthworms sometimes meal worms (plant-eating insect larvae)?
                  • Why don’t any of these people act like real scientists, or at least like professionals?
                  • Why is this movie rehashing trite 1950′s cliches about the difference between humans and robots being emotion and curiosity after just showing us the android character having feelings and being curious?
                  • If the aliens engineered life on earth, then doesn’t making it look like a species of primate evolved to have the same genome as them seem wierdly narcissistic?
                  • How did that alien get so big so fast without eating anything?

                  It’s okay for movies to leave things unexplained. I’m cool with that. Scientists live in a world full of unexplained things. I’d even prefer that movies to leave things unexplained and just chalk it up to alien technology or whatever. Their explanations are usually boring and stupid, anyway. But when the plot revolves around events that any undergrad biology major could poke holes in, well, it’s hard to get past that. Prometheus apparently did have a science consultant, though his involvement seems to have been limited a single conversation. Film makers hire people whose whole job it is to make sure that continuity of appearance is maintained from shot to shot. Can’t they hire someone to make sure the science makes sense, too? Or at least isn’t unnecessarily egregious? Please?

                  I don't understand, therefore aliens (and/or God)

                  The worst part of Prometheus for me, though, is that the movie is anti-science without even realizing it. Like, for example, the part where the main protagonist couple (archaeologists) claim that aliens engineered life on earth. One of the other characters reasonably asks what evidence they have for this, and the main protagonist says “It’s what I choose to believe”. Ugh. The movie presents this as a heroic act rather than, you know, pants-on-head retarded. To be clear here, this is the equivalent of a professional archaeologist saying that aliens built the pyramids of Egypt. The only response other characters have to this conspiracy idiocy is to whine that it goes against “Darwinism”—as if evolutionary biology were a philosophical belief rather than, you know, science supported by observable facts. In spite of this, the filmmakers inexplicably believe that one of their protagonists is a skeptic.

                  Dear Mr. Scott: Richard Feynman was right when he said “Science is a long history of learning how not to fool ourselves.” What you show us is self-deception of the worst kind.

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                    Don’t bury the lead

                    The conference I was at last week included a session in which each poster presenter was given a one-slide, 60-second slot—a movie trailer for their poster, you might say. It’s an interesting approach, though I’m still unsure whether I like it or not. One thing that really struck me was that the vast majority of presenters never even stated what their main finding was. Most of them were like, “Here’s the general topic of my poster and the organism I study. If you want to hear more, come by my poster.” I found it frustrating as both as a potential poster viewer and as someone who believes in taking scientific communication seriously. So please, poster presenters and papers writers of the world:

                    Scientists already struggle with a deluge of more papers, posters, and talks than they could ever feasibly process. Give them reason to believe their time and attention will be well-spent on yours.

                    Also, the whole “if you want to hear more, come by my poster” bit is just wasted time and breath. Advertisements for toothpaste don’t bother saying “Buy FluoroWhite Tooth Creme if you want teeth like this!” because they know their viewers already recognize the ad for what it is—an ad.

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                      Setting the right tone

                      I’ve been finding that the problems reviewers sometimes have with my papers is not so much the actual experiments or the conclusions drawn from them, but rather the tone with which they are presented. Take this passage from a paper we’re revising:

                      To avoid telling “just-so stories”, researchers studying adaptation should actively identify, test, and exclude alternative hypotheses. As George Williams famously put it, “adaptation is a special and onerous concept that should not be used unnecessarily, and an effect should not be called a function unless it is clearly produced by design and not by chance”. Selection is an important mechanism of evolution, but not the only one. Nonadaptive mechanisms like mutation and drift can also play important roles. Mechanisms by which individuals may directly benefit from expressing a trait should also be explored.

                      This passage seems to evoke strong emotional responses from some people (and not because of the awkward passive voice at the end). I thought we were just describing good scientific practice for studying adaptation. The reviewers apparently thought it was patronizing. One reader even thought that using the phrase “just-so stories”, automatically counted as a full endorsement of Steven J. Gould & Dick Lewontin’s attack on behavioral ecology. The passage seems to make some people really defensive, so I think for the sake of the paper we’re going to take it out. Which is too bad, since I think it’s a message that some researchers could stand to hear (or hear again).

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                        Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae

                        Plasmids are mobile bits of DNA that play a key role in bacterial evolution. They shuttle genes for things like antibiotic resistance and pathogen virulence among different strains or species of bacteria. But not all plasmids carry these genes, and it’s been an outstanding question how plasmids persist in bacterial populations. One possibility is that they’re a kind of genetic parasite, slightly reducing the fitness of the cells they infect but continually infecting new bacteria. One problem with this idea, though, is that their infection rates often don’t seem high enough for a purely parasitic lifestyle.

                        A new paper by Bärbel Stecher and colleagues at ETH Zürich shows that when Salmonella infect mammalian guts they create an environment that drastically increases plasmid transfer among the bacteria there. They inflame the gut tissue, causing a “bloom” of resident E. coli. All those bacteria bump into each other more, allowing plasmids—which spread by direct contact between bacterial cells—to go gangbusters. The paper has a lot of good experiments showing that it’s the increased bacterial density, and not the inflammation, that causes increased plasmid transfer.

                        The implication is that plasmids can make a living as parasites if Salmonella and other pathogens cause enough gastrointestinal disturbance, as they might in the developing world or in nonhuman mammal populations. I did find overblown the authors’ claims that their findings “shift the current paradigm” because they show that Salmonella and E. coli share plasmids (which we already knew) and “boost” pathogen evolution (which their findings do not show), but overall this is a pretty cool paper.

                        Stecher B, Denzler R, Maier L, Bernet F, Sanders MJ, Pickard DJ, Barthel M, Westendorf AM, Krogfelt KA, Walker AW, Ackermann M, Dobrindt U, Thomson NR & Hardt W-D (2012) Gut inflammation can boost horizontal gene transfer between pathogenic and commensal Enterobacteriaceae. PNAS 109: 1269-1274.

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                          What makes something “systems biology”?

                          Like many biologists, I’ve wondered at times what the relatively new discipline of systems biology is all about. A lot of things get called systems biology, from genomics to metabolism to gene regulation. I often find the systems biology approaches to these fields pretty interesting, even when it’s fairly removed from my research area. Like myself, systems biologists often have a background in physics. Sometimes systems biology even includes microbial cooperation. So what ties it all together?

                          Well, systems biology:

                          • studies dynamic, complex systems whose behavior is governed by the interactions of their component parts
                          • uses quantitative, data-rich measurements of dynamical behavior
                          • uses mathematical and computational models to predict and analyze dynamical behavior

                          Viewed this way, I would argue that systems biology has a lot in common with ecology and evolutionary biology. In some ways, it’s just population biology applied to molecules and cells rather than individuals and species. Asking how genetic regulatory circuits create persistent cycles of gene expression rather than coming to some stable equilibrium is not all that different than asking how predator/prey dynamics create population cycles rather than coming to some stable equilibrium. And with any luck, systems biology will help bridge the traditional divide between population biologists and their more mechanistic colleagues.

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                            Tragedy of the commons among antibiotic resistance plasmids

                            smith (2012) Tragedy of the commons among antibiotic resistance plasmids. Evolution. doi: 10.1111/j.1558-5646.2011.01531.x | Early view at Evolution

                            Abstract: As social interactions are increasingly recognized as important determinants of microbial fitness, sociobiology is being enlisted to better understand the evolution of clinically relevant microbes and, potentially, to influence their evolution to aid human health. Of special interest are situations in which there exists a “tragedy of the commons,” where natural selection leads to a net reduction in fitness for all members of a population. Here, I demonstrate the existence of a tragedy of the commons among antibiotic resistance plasmids of bacteria. In serial transfer culture, plasmids evolved a greater ability to superinfect already-infected bacteria, increasing plasmid fitness when evolved genotypes were rare. Evolved plasmids, however, fell victim to their own success, reducing the density of their bacterial hosts when they became common and suffering reduced fitness through vertical transmission. Social interactions can thus be an important determinant of evolution for the molecular endosymbionts of bacteria. These results also identify an avenue of evolution that reduces proliferation of both antibiotic resistance genes and their bacterial hosts.

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                              “A whole dimension of completely weird, incredible things”

                              Errol Morris has this “Op-Doc” (Opinion-Documentary, as odd as that sounds) over at the New York Times. Technically, it’s about historical research, but I think the phenomenon holds true for biology, as well. As interviewee Tink Thompson puts it, “if you put any event under the microscope, you will find a whole dimension of completely weird, incredible things going on”. I’ve always loved Morris’ work. A lot of it deals with issues of knowing—how we know what we know, how we sometimes deceive ourselves, what evidence does or doesn’t say. Important issues for any scientist.

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